1. Field of the Invention
The present invention relates generally to computerized methods for processing images, and particularly to a method of performing fingerprint matching having an algorithm based on matching that combines directional features with moment invariants.
2. Description of the Related Art
Fingerprint matching techniques can be cast into four broad classes, namely, minutiae, correlation, Ridge features (texture), and Transform-based techniques. However, considering the types of information used, a method can be broadly categorized as minutiae-based or texture-based. While minutiae-based fingerprint verification systems have shown high accuracy, they ignore the rich information in ridge patterns, which can be useful to improve matching accuracy.
Texture-based fingerprint matching techniques include those that use fingerprint ridge features. Methods exist that estimate the nonlinear distortion in fingerprint pairs based upon ridge curve correspondences.
Exemplary prior art filter banks and directional filter banks employ a filter-based algorithm using a bank of Gabor filters. Gabor filters are used to capture local and global details of a fingerprint into a fixed finger code. Fingerprint matching is then performed using the Euclidean distance between two corresponding finger codes.
Another approach, which is based on DFB filtering, decomposes the fingerprint image into eight directional sub-band outputs. This creates a frequency decomposition of the input and output that can be used to create a filtered image output. Directional energy distributions for each block are then extracted from the decomposed sub-bands. To reduce noise effect and improve efficiency, only dominant directional energy components are kept as elements of the input feature vector. Additional input feature vectors in which various rotations are considered are extracted, and these input feature vectors are compared with an enrolled template feature vector.
The aforementioned texture and transform-based matching methods have advantages dealing with such images, as they utilize features that are not based solely on minutiae templates. However, it should be noted that one main advantage of minutiae-based approaches is that they are faster. There remains a need for a fast texture and transform-based matching model.
Thus, a method of performing fingerprint matching solving the aforementioned problems is desired.